Our network is trained with the KITTI dataset alone.
This code was tested with Python 3 and PyTorch 1.0 on Ubuntu 16.04.
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Install PyTorch on a machine with CUDA GPU.
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The code for self-supervised training requires OpenCV along with the contrib modules. For instance,
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Download the KITTI Depth Dataset and the corresponding RGB images.
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The code, data and result directory structure is shown as follows
.
├── self-supervised-depth-completion
├── data
| ├── kitti_depth
| | ├── train
| | ├── val_selection_cropped
| └── kitti_rgb
| | ├── train
| | ├── val_selection_cropped
├── results
A complete list of training options is available with
python main.py -h
For instance,
python main.py --train-mode dense -b 1 # train with the KITTI semi-dense annotations and batch size 1
python main.py --resume [checkpoint-path] # resume previous training
python main.py --evaluate [checkpoint-path] # test the trained model
python main.py --evaluate ./model_best.pth.tar
[1] @article{liu2020plin,
title={Plin: A network for pseudo-lidar point cloud interpolation},
author={Liu, Haojie and Liao, Kang and Lin, Chunyu and Zhao, Yao and Liu, Meiqin},
journal={Sensors},
volume={20},
number={6},
pages={1573},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute}
}